SOTAVerified

3D Object Detection

3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.

( Image credit: AVOD )

Papers

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Showing 876–900 of 1576 papers

TitleStatusHype
PSA-Det3D: Pillar Set Abstraction for 3D object Detection—0
Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training—0
Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection—0
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel LogisticsCode1
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection—0
Bridging the Domain Gap for Multi-Agent PerceptionCode1
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining—0
SAILOR: Scaling Anchors via Insights into Latent Object RepresentationCode0
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds—0
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds—0
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point CloudsCode1
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object DetectionCode2
Bridged Transformer for Vision and Point Cloud 3D Object DetectionCode0
D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud SequenceCode1
PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object DetectionCode1
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment—0
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection—0
Out-of-Distribution Detection for LiDAR-based 3D Object Detection—0
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object DetectionCode1
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents—0
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence MapsCode2
Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects—0
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars—0
FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object DetectionCode0
BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal StereoCode2
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All datasetsnuScenesScanNetV2SUN-RGBD valKITTI Cars EasyKITTI Cars HardnuScenes Camera OnlyKITTI Cars Easy valKITTI Cars Moderate valnuscenes Camera-RadarView-of-Delft (val)KITTI Cars Hard valDAIR-V2X-I

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MMFusion-eNDS0.77—Unverified
3MegFusionNDS0.77—Unverified
4RacoonPowerNDS0.76—Unverified
5BEVFusion-eNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8DAANDS0.75—Unverified
9FusionVPENDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified